A Practical Introduction to Econometric Methods: Classical and Modern
$9.99

A Practical Introduction to Econometric Methods: Classical and Modern

By Patrick K. Watson, Sonja S. Teelucksingh
US$ 9.99
The publisher has enabled DRM protection, which means that you need to use the BookFusion iOS, Android or Web app to read this eBook. This eBook cannot be used outside of the BookFusion platform.
Book Description
Table of Contents
  • Contents
  • Foreword
  • Preface
  • Introduction
    • Introduction
    • Classical and Modern Econometrics
    • Exercises
  • Part I: Classical
    • Chapter 1: The General Linear Regression Model
      • Models in Economics and Econometrics
      • Data and Econometric Models
      • Specifying the Model
      • Introducing the Error Term
      • Desirable Properties of the Error Term
      • The General Linear Regression Model
      • Ordinary Least Squares
        • Special Case: k = 2 and x1t = 1 for all t
      • Numerical Calculation
      • Forecasting with Econometric Models
      • The Gauss-Markov Theorem on Least Squares
        • Proof of Part (1) of the Theorem
        • Proof of Part (2) of the Theorem
        • Proof of Part (3) of the Theorem
      • Understanding the Lessons of the Gauss-Markov Theorem
      • Exercises
      • Appendix 1.1: Moments of First and Second Order of Random Variables and Random Vectors
        • Random Variables
        • Random Matrices and Vectors
        • Application to the General Linear Regression Model
      • Appendix 1.2: Time Series Data for Trinidad and Tobago 1967-1991
    • Chapter 2: Evaluating the Ordinary Least Squares (OLS) Regression Fit
      • Some Preliminary Remarks
      • The Coefficient of Determination and the Adjusted Coefficient of Determination
      • Confidence Intervals for Coefficients
      • Significance Tests of Coefficients
      • Testing the Simultaneous Nullity of the Slope Coefficients
      • “Economic” Evaluation of Regression Results
      • Reporting Regression Results
      • Exercises
    • Chapter 3: Some Issues in the Application of the General Linear Regression Model
      • Multicollinearity: the Problem
      • Multicollinearity: Detection
      • Multicollinearity: a Solution?
      • Multicollinearity: an Illustration
      • Misspecification
      • Dummy Variables
      • Illustration Involving a Dummy Variable
      • Exercises
    • Chapter 4: Generalized Least Squares, Heteroscedasticity and Autocorrelation
      • Generalized Least Squares
      • Properties of the Generalized Least Squares Estimator
      • Consequences of Using Ordinary Least Squares When u ~ (0, s2V)
      • GLS Estimation: a Practical Solution?
      • Ad Hoc Procedures for the Identification of Heteroscedasticity and Autocorrelation
        • Heteroscedasticity: Some Further Considerations
        • Heteroscedasticity: Testing for its Presence
          • The Goldfeld-Quandt Test
          • The Koenker Test
          • Illustration of the Koenker Test for Heteroscedasticity
        • Other Tests for Heteroscedasticity
        • Estimation in the Presence of Heteroscedasticity
        • Autocorrelation: The Problem
        • Autocorrelation: Testing for its Presence Using the Durbin-Watson Statistic
        • Some Justification for the Mechanism of the Durbin-Watson Test
        • An Illustration of the Durbin-Watson Test for Autocorrelation
        • Other Tests for Autocorrelation
        • Estimation in the Presence of Autocorrelation
          • The Cochrane-Orcutt Procedure
          • The Hildreth-Lu Procedure
          • The EViews Procedure
      • Autocorrelation and Model Specification: a Word of Caution
      • Exercises
    • Chapter 5: Introduction to Dynamic Models
      • Dynamic Models
      • Almon’s Polynomial Distributed Lag (PDL) Scheme
        • Illustration of Almon’s Polynomial Distributed Lag Scheme
      • The Koyck Transformation
        • Illustration of the Koyck Transformation
      • The Partial Adjustment Model
      • The Adaptive Expectations Model
      • Error Correction Mechanism (ECM) Models
        • Illustration of the Error Correction Mechanism Model
      • Autoregressive Distributed Lag (ADL) Models
        • Illustration of the Autoregressive Distributed Lag Model
      • The Durbin Test for Autocorrelation in the Presence of Lagged Endogenous Variables
        • Illustration of the Durbin h-Test
      • Exercises
    • Chapter 6: The Instrumental Variable Estimator
      • Introduction
      • Consistent Estimators
      • Is OLS Consistent?
      • The Instrumental Variable Estimator
      • The Errors in Variables Model
      • Exercises
    • Chapter 7: The Econometrics of Simultaneous Equation Systems
      • Introduction
      • Identification
      • Identifiability of an Equation and Restrictions on the Structural Form
        • Conditions of Identifiability of an Equation
      • Estimation in Simultaneous Equation Models
        • Consistency of the Two Stage Least Squares Estimator
        • The Two Stage Least Squares Estimator as an Instrumental Variable Estimator
        • Equivalence of Two Stage Least Squares and Indirect Least Squares in the Case of an Exactly Identified Equation
        • Illustration of the Two Stage Least Squares Estimator
      • Exercises
    • Chapter 8: Simulation of Econometric Models
      • Introduction
      • Dynamic and Static Simulation
      • Some Useful Summary Statistics
        • Root Mean Square Error
        • Mean Absolute (or Mean Difference) Error
        • The Theil Inequality Coefficient
        • The Theil Decomposition
        • Regression and Correlation Measures
      • Some Illustrations of the Use of Model Simulation
        • Evaluation of Goodness-of-Fit of Single Equation Systems
        • Forecasting with Single Equation Systems
        • Evaluation of Goodness-of-Fit of Multiple Equation Systems
      • Dynamic Response (Multiplier Analysis) in Multiple Equation Systems
        • Illustration of Dynamic Response
        • Forecasting and Policy Simulations with Multiple Equation Systems
        • Illustration
      • Exercise
  • Part II: Modern
    • Chapter 9: Maximum Likelihood Estimation
      • Introduction
      • The Cramer-Rao Lower Bound (CRLB)
      • Properties of Maximum Likelihood Estimators
      • Maximum Likelihood Estimation in the General Linear Regression Model
      • Exercises
    • Chapter 10: The Wald, Likelihood Ratio and Lagrange Multiplier Tests
      • Introduction
      • Defining Restrictions on the Parameter Space
      • The Likelihood Ratio Test
      • The Wald Test
      • The Lagrange Multiplier Test
        • Illustration: Test of Parameter Redundancy
        • Illustration: Testing Restrictions on Coefficient Values
      • Conclusion
      • Exercises
    • Chapter 11: Specification (and Other) Tests of Model Authenticity
      • Introduction
      • Ramsey’s RESET Test for Misspecification (Due to Unknown Omitted Variables)
        • Illustration of the Ramsey RESET Test
      • The Jarque-Bera Test for Normality
        • Illustration of the Jarque-Bera Test for Normality
      • The Ljung-Box and Box-Pierce Tests for White Noise
        • Illustration of the Ljung-Box Test
      • The White Test for Heteroscedasticity
        • Illustration of the White Heteroscedasticity Test
      • The Breusch-Godfrey Test for Serial Correlation
        • Illustration of the Breusch-Godfrey Test for Serial Correlation
      • The Chow Test for Structural Breaks
        • Illustration of the Chow Test for Structural Breaks
      • Exercises
    • Chapter 12: Stationarity and Unit Roots
      • The Concept of Stationarity
      • Unit Roots: Definition
      • Looking for Unit Roots: an Informal Approach
      • Formal Testing for Unit Roots
      • Exercises
    • Chapter 13: An Introduction to ARIMA Modelling
      • Introduction
      • ARIMA Models
        • Autoregressive Processes of Order p AR(p)
        • Moving Average Processes of Order q MA(q)
        • Autoregressive Moving Average Processes of Order p, q ARMA(p, q)
        • Autoregressive Integrated Moving Average Processes of Order p, d, q ARIMA(p, d, q)
      • The Partial Autocorrelation Function (PACF)
      • Estimating the Autocorrelation and Partial Autocorrelation Functions
        • Estimation of the Mean
        • Estimation of the Autocovariance of Order k
        • Estimation of the Autocorrelation of Order k
        • Estimation of the Partial Autocorrelation of Order j
        • Sampling Distributions of and
      • The Box-Jenkins Iterative Cycle
        • Identification
        • Illustrating the Identification of p and q
        • Estimation and Diagnostic Checking
        • Illustration of the Estimation and Diagnostic Checking Phases
        • Forecasting
        • Illustration of the Forecasting Phase
      • Seasonal Models
      • Exercises
      • Appendix 13.1
    • Chapter 14: Vector Autoregression (VAR) Modelling with Some Applications
      • Introduction
      • Vector AutoregressiON Models
      • Illustration of vector autoregression estimation using eviews
      • Evaluation of Vector Autoregression Models
        • The Impulse Response Function
        • Variance Decomposition
      • Forecasting with Vector Autoregression Models
        • Illustration of Forecasting with Vector Autoregression Models
      • Vector Autoregression Modelling and Causality Testing
      • Testing for Causality
        • Direct Granger Tests
        • Illustration of Direct Granger Tests
        • The Sims Test
      • Exercises
      • Appendix 14.1
    • Chapter 15: Cointegration
      • Introduction
      • The Vector Error Correction Model (VECM)
      • The Engle-Granger (EG) Two-Step Procedure
        • Illustration of the Engle-Granger Two-Step Procedure
        • Strengths and Weaknesses of the Engle-Granger Two-Step Procedure
      • The Johansen Procedure
        • Estimation of a and b
        • Testing for the Cointegrating Rank r
        • Illustration of the Johansen Procedure
      • Cointegration and Causality
      • Exercises
      • Appendix 15.1: Critical values for ADF tests of cointegratability
        • (constant term included in test equations)
  • Appendices
    • Statistical Tables
  • References
  • Index
  • Untitled
      No comment for this book yet, be the first to comment
      You May Also Like